Joined November 2016
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This is an ongoing thread for my series, "30 Essays to Make You Love Biology." ❤️🧬 I'll pin it on my profile.
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Niko McCarty. reposted
Application deadline next week—July 10th!
Announcing the Hyperspectral Biology Fund: It's possible to see engineered microbes from space; not with the naked eye, but by the distinctive way their molecules absorb light. The secret is hyperspectral cameras. Whereas a normal camera captures just three colors (red, green, and blue), a hyperspectral camera captures the full spectrum of light at every pixel. When merged with synthetic biology, one can create engineered organisms that sense something (a pathogen, explosive, pollutant, etc) and then monitor them using satellites with hyperspectral cameras orbiting Earth. The end goal is to build a planetary-scale biosensing network. I'm giving away $75,000 in microgrants to help grow this field. Applications are due by July 10. Thanks to @davidtlang and the Experiment Foundation for making this possible.
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Follow their progress @Bioticorg
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Niko McCarty. reposted
Today we're launching Biotic.org: a public-benefit organization building the foundations for engineering biology in the open. 🧵
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If you like this, then you may like my blog about biology: nikomc.com.
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Out today: Researchers have made the first cell capable of “feeding, growth, replication, division and selection...entirely using components scientists put there.” In other words, a cell that self-replicates and was made entirely from the ground-up, molecule by molecule. The cell is called SpudCell. And although it is definitely a cell (in that it has a membrane with molecules inside) it is definitely not alive, because it cannot grow indefinitely, survive without human help, make its own ribosomes, or recycle waste. It "dies" after a few divisions. But it's a starting point! And these same researchers have raised $6-8M in philanthropic funding to scale their efforts with a new nonprofit for synthetic cell research, called Biotic.
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Niko McCarty. reposted
I’m so excited to share this update on @Conception – We’ve generated the first early human eggs derived from stem cells. This is a big deal -- the potential to redefine fertility is real.
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Richard Murray, a professor at Caltech, made this beautiful chart showing how the complexity of gene circuits (as measured by their number of "parts," or components) has scaled over time. (I'm sharing it below with permission.) We can learn many things from this chart. First, academic laboratories have been able to make some *really* complicated gene circuits. My friend, Jai Padmakumar, made the largest gene-circuit ever reported; it was described in a 2024 paper. Jai assembled 1.1 million bases of synthetic DNA into 110 distinct logic gates, and then partitioned that DNA across 66 strains of E. coli. Together, these engineered cells could compute the MD5 hashing algorithm. The problem is that the larger you make your gene circuit, the less "robust" or reliable the engineered cell becomes. Living organisms did not evolve to carry human-made gene circuits! Therefore, many synthetic biology efforts fail to scale into the real-world. The more complex a gene circuit, or the more genes it has, the less likely that it will be robust over time. More genes have more opportunities to break. (Note that this is not always the case in natural organisms. Many cells have evolved overlapping ways to regulate genes, such that if one breaks, others can fill in the gap. We're not good at emulating this synthetically, though.) The chart below shows this trend via the red, dotted line. Engineered cells that have been *commercialized* tend to have only a small number of engineered components; usually less than 10 synthetic genes in total. There is a drop-off in number of components as we move from the laboratory to the real-world. How can synthetic biologists solve this, and begin to build large gene circuits that are robust over time? Perhaps we should make it standard to grow engineered cells in a small bioreactor, perturb them with various stressors, and see how well the engineered cells hold up over time. We could record the number of generations that pass before a cell's functions break, and then report that value in the paper. (This is sometimes done, but not often.) Another option is to "merge" human-made designs, or AI-generated DNA, with continuous evolution. If we wanted to engineer a cell to break down plastic and recycle the atoms into a medicine, for example, then we could first build dozens of different gene circuit architectures (using high-throughput DNA assembly methods), put each gene circuit into a cell, and then do continuous evolution on each of them to see which one holds up best over time, with various stressors. We could sequence the populations over time, see which sequences hold up well, and use the data to train predictive models of "cellular robustness."
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Niko McCarty. reposted
SITUATION EXPLAINED: What happens to biology when AI can design things we don't understand and can't explain? We asked @NikoMcCarty, founding editor of @AsimovPress: "It's not that biology is irreducibly complex. Biology is governed by the laws of molecules and the laws of physics. Cells are not sacred. They're bags of molecules." "For synthetic biology to scale, for us to build very complex functions programmably inside of living cells, we probably have to get out of this reductionist historical approach where we want to understand all the genes that we are building with." "We're just gonna see much, much more use of AI to design circuits and cell functions that we don't really mechanistically understand." "There will probably be a schism where people will be using AI to design biology, and then others will try to understand why that works. You kind of try to solve the mechanism after you've already made the thing." "There will always be people who want to build things from the bottom up. I wanna understand everything I'm building with. It's just, I think that progress will be slow because it has been slow for the last twenty-five years."
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Niko McCarty. reposted
on the show today: - @timhwang on leading fai legal - @signulll on consumer ai - @NikoMcCarty on hyperspectral biology - @samhogan on frontier lab revenue multiples - @DanielleFong on the latest meta / fable updates - @RobCSlaughter on antimatter propulsion - @terrry on techdollar for private co employees - @braeden_norris on his latest report on glm-5.2 and more as we book folks throughout the day. come monitor the situation with us
META BRAIN MODEL | NEW AGENT BILL | FABLE BACK SOON? x.com/i/broadcasts/1nxeLLOWp…
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I think biotechnology is the most exciting thing you can work on. But I have never written down my reasons for thinking this explicitly, so here are some bulleted arguments: - It is incredibly broad across both space and time. Biology spans every possible scale, from atoms to organisms to the planet. Biochemical reactions happen at nanosecond timescales, whereas organisms evolve over hours (in some cases) or millennia. This breadth means that there is always something new to discover. I believe it's possible for any undergraduate student, with a little bit of guidance, to make an original discovery in a matter of weeks. It may not be an important discovery, but you can quickly find things that nobody else has ever found before. - Insights gained into one organism apply to many others. It is a miracle that we can engineer living organisms at all. How peculiar that a bacterial defense system (CRISPR-Cas) can be adapted into a gene-editing tool which works not only in bacteria, but also in plants and algae and humans. All life shares a common ancestor, and is assembled from a common set of ingredients, so we can mix-and-match our tools to solve incredibly diverse problems. (If life formed multiple times, and each "tree" of life persisted to the present, then the tools made for one branch of that tree would be unlikely to work in another. Fortunately, this is not the case.) - A deep understanding of biology can be applied to a *huge* range of problems. Say you're a protein designer, using computers to design new types of molecules. Such a skill is not only useful for making medicines! It can also be used to make antivenoms, or to design peptides that protect plants against pests, or a million other things. This means that, as a biotechnologist, you can work on medicines or climate change or agriculture or making life multiplanetary ... all using a common set of skills. It is just unfortunate that biology is taught in such a boring way in schools, with textbooks and rote memorization. My advice would be to fight through your boredom and then join a research laboratory, as soon as you possibly can. Get hands-on skills and try to work on your own problems.
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If you like this, then you may like my blog about biology: nikomc.com.
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Genome length strongly correlates with cell size; even more than cell complexity, number of protein-coding genes, etc. This was extremely surprising to me. Initially, I expected that genome size would correlate with protein-coding genes. Although there is a linear relationship in prokaryotes (which have compact genomes with fewer regulatory elements), there is a logarithmic relationship in eukaryotes. A eukaryote called Edhazardia aedis, for example, has a genome with ~51 million nucleotides that only encodes 4,200 proteins. Some bacteria have genomes that are about an order-of-magnitude smaller in length, yet encode more proteins than this! And what of cell complexity? Surprisingly, there is no relationship between genome length and complexity. Eukaryote genomes range in size by 200,000-fold. There are amoebas, salamanders, and small plants with genomes much larger than our own. An onion’s genome is five times larger than a human’s. The closest correlation — and one that scales across kingdoms of life — is between genome length and cell size. Many papers on this subject have been written by T. Ryan Gregory, a Canadian biologist, who has collected thousands of examples of genome sizes and cell sizes. Gregory also maintains a database on genome sizes across the tree of life, at genomesize[dot]com. In a 2007 paper, Gregory plotted this relationship for red blood cells taken from various organisms, such as fishes, amphibians, reptiles, and birds. (Red blood cells were selected so that each “type” of cell would be standardized across the organisms.) See chart #1 below. Many recent papers continue to show the same relationship. I downloaded raw data from a 2023 paper, for example, that lists genome sizes and cell volumes for thousands of bacteria and eukaryotes. 53 organisms in this dataset have both a recorded cell volume *and* genome size, and those points are plotted in the second chart below. The question is why this relationship exists at all. What does genome size have to do with cell size? Many biologists argue for some kind of physical scaling. The size of a cell’s nucleus corresponds closely with its overall size, and most cells keep their “nuclear-to-cytoplasmic volume ratio” at a constant level. The more DNA a cell has, then, the more space it occupies, and the larger its nucleus (and overall cell size) must be to maintain this ratio. This explanation is unsatisfying. For one, bacteria don’t have a nucleus, so why does this scaling apply to them? And second, the genome typically occupies less than 1% of the total nucleus volume, so why would a larger genome lead to a bigger nucleus mechanistically? There is plenty of space in there! (Sidebar: A tiny fern from a South Pacific island has the world’s largest genome: 160.45 billion bases, more than 50-times larger than a human genome. If stretched out, this genome would be longer than the Statue of Liberty is tall; and yet, it occupies only a small portion of the fern’s nucleus.) The reality seems to be that biologists don’t really understand (to a satisfying degree) why this relationship is true. Simple questions in biology often yield exceptionally complex answers.
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Some journals are using bots to automatically "retract" papers, but those bots often make mistakes. Two papers by Max Planck, the 1918 Nobel Prize-winning physicist, have been retracted by Springer Nature for false copyright violations. In one case, Max Planck published a paper in multiple different journals (which "was widespread before the internet"). A modern copyright bot flagged these duplicates and retracted them. This bit, from the news article about the story, is both hilarious and sad: "...Springer Nature deviated from the normal practice of merely slapping the word RETRACTED across the digital version of the paper while still allowing scholars to read the text. Instead, the publisher posted a blank white page with the cryptic phrase, 'This article has been withdrawn due to article violation.' Springer Nature is nevertheless still selling the empty PDF for $39.95."
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Thomas Astle also wrote a history of "early microplate automation."
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